On-Line Analytical Processing (OLAP) generally refers to a technique of providing fast analysis of multi-dimensional data. OLAP provides a multi-dimensional conceptual framework for data that may include support for hierarchies. This conceptual framework is advantageous since it often provides the most logical way to organize data relating to businesses or other types of organizations.
Typically, OLAP involves analyzing data stored in a multi-dimensional database. The multi-dimensional database may organize data in multiple dimensions and multiple fields along a given dimension. For example, a business may employ a five-dimensional database storing six months of weekly data relating to sales figures for fifty products that are sold in ten regions by five outlets. A user may be interested in identifying patterns associated with the sales figures in order to guide a decision-making process for the business. For instance, the user may be interested in identifying trends or unusual values associated with the sales figures. Even for this relatively simple five-dimensional database, 2500 separate time series may need to be analyzed. If additional fields or dimensions are included, the number of time series to be analyzed may be voluminous.
A given corpus of data in a multidimensional database may exhibit more than one type of data pattern. For instance, outlier patterns, step change patterns, trend patterns, random patterns and periodic patterns are just a few examples. It may be beneficial to compare these patterns to each other in order to determine which are more significant in order to make decisions based on the most significant of the patterns. Thus, there is a need for a common measure to compare disparate types of patterns to determine their significance.